1. Field of the Invention
The present invention relates generally to a method and apparatus for controlling microelectromechanical systems (MEMS), and more particularly, to a control system having a global controller and local agents for controlling movement of an object on a transport assembly.
2. Description of Related Art
Smart matter is defined herein as a physical system or material with arrays of microelectromechanical devices embedded therein for detecting and adjusting to changes in their environment. For example, smart matter can be used to move sheets of paper in a printing machine or maneuver an aircraft by performing tiny adjustments to wing surfaces. Generally, each microelectromechanical device embedded in smart matter contains microscopic sensors, actuators, and controllers. A characteristic of smart matter is that the physical system consists large numbers (possibly thousands) of microelectromechanical devices. These devices work together to deliver a desired higher level function (e.g., moving a piece of paper from one location to another, or flying a plane).
Programs for controlling smart matter do not always adequately achieve the desired higher level function of issuing command to compensate for detected changes in a physical system because of the significant number of devices that operate in parallel to control it. That is, there exists a number of factors which make the computational task of a control program for smart matter difficult. One factor which may be cause control programs to be computationally intense is caused by the high redundancy of sensors and actuators in the physical material. In order for smart matter systems to exhibit the enhanced reliability and robustness over conventional systems, smart matter systems contain many more devices than necessary to achieve a desired performance. Failure or improper function of some elements, even a significant fraction, is compensated by the actions of the redundant components. Moreover, the ability of smart matter systems to tolerate component failure can be used beneficially to lower the fabrication cost of the components.
One approach for controlling smart matter is to rely on a single global processor coupled with rapid access to the full state of the system and detailed knowledge of system behavior. This method, however, is generally ineffective because of the large number of devices embedded in smart matter. Another approach for controlling smart matter is through the use of a collection of autonomous computational agents (or elements) that use sensor information to determine appropriate actuator forces. Using multiple computational agents to provide distributed control instead of centralized control may prove more effective because each computational agent is only concerned with limited aspects of the overall control problem. In some multi-agent systems, individual agents are associated with a specific sensor or actuator embedded in the physical system. This method for controlling smart matter defines a community of computational agents which, in their interactions, strategies, and competition for resources, resembles natural ecosystems. Furthermore, by distributing control among computational agents, the system as a whole is better able to adapt to environmental changes or disturbances because the system can compensate for new circumstances by simply changing the relationship of the agents.
Although multi-agent control systems have been used to solve distributed control problems, they have been limited to systems which are physically large or geographically scattered. For example, multi-agent systems have been used in distributed traffic control, flexible manufacturing, robotic system design, and self-assembly structures. Using multi-agent systems to control smart matter is different from these known multi-agent systems because of the tight coupling between computational agents and their embedded physical space. Furthermore, controlling smart matter using traditional multi-agent systems is difficult because of mechanical interactions that decrease in strength with the physical distance between them. This makes the computational problem difficult because interactions between computational agents cannot be ignored.
In defining a multi-agent control systems for controlling smart matter, there exits a need to identify a distributed control organization with agents that interact locally while robustly performing a global goal that is specified using global constraints on the system. It would, therefore, be desirable to provide a control system for controlling smart matter that is capable of rapidly responding to local perturbations while robustly satisfying the global goal. In addition, it would be desirable to provide a controller for smart matter that robustly coordinates a physically distributed real-time response with many devices in the face of failures, delays, changing environmental conditions, and incomplete models of system behavior.